Do All Birds Tweet the Same? Characterizing Twitter Around the World

Do All Birds Tweet the Same?
Characterizing Twitter Around the World
Barbara Poblete1,2
Ruth Garcia3,4
Marcelo Mendoza5,2
Alejandro Jaimes3
{bpoblete,ruthgavi,mendozam,ajaimes}@yahoo-inc.com
1
Department of Computer Science, University of Chile, Chile
2
Yahoo! Research Latin-America, Chile
3
Yahoo! Research Barcelona, Spain
4
Universitat Pompeu Fabra, Spain
5
Universidad Técnica Federico Santa María, Chile
ABSTRACT
1.
Social media services have spread throughout the world in just a
few years. They have become not only a new source of information, but also new mechanisms for societies world-wide to organize
themselves and communicate. Therefore, social media has a very
strong impact in many aspects – at personal level, in business, and
in politics, among many others. In spite of its fast adoption, little is known about social media usage in different countries, and
whether patterns of behavior remain the same or not. To provide
deep understanding of differences between countries can be useful
in many ways, e.g.: to improve the design of social media systems
(which features work best for which country?), and influence marketing and political campaigns. Moreover, this type of analysis can
provide relevant insight into how societies might differ. In this paper we present a summary of a large-scale analysis of Twitter for
an extended period of time. We analyze in detail various aspects
of social media for the ten countries we identified as most active.
We collected one year’s worth of data and report differences and
similarities in terms of activity, sentiment, use of languages, and
network structure. To the best of our knowledge, this is the first
on-line social network study of such characteristics.
The use of social media has grown tremendously all over the
world in recent years, and the impact of such growth has expanded
in unexpected ways. Twitter, in particular, has become the most
widely used microblogging service, and messages posted on it,
in many ways, reflected real life events– from the revolutions in
Tunisia and Egypt, to natural disasters such as the Chilean and
Japanese earthquakes. Twitter users, however, post and share all
kinds of information, ranging from personal opinions on important
political issues, to mundane statements that may have little interest
to most, except for their closest friends.
The range and scope of the service, and the fact that most user
profiles and tweets are public, creates a huge opportunity for researchers. Using Twitter data, they can gain insights not just into
how that particular service is used, but also into questions that are
relevant in a social system for a particular point in time. This includes how news propagates, how people communicate, and maybe
how they influence each other. Given this context, two key questions in the study of social media are how its use differs across
cultures and countries, and whether any patterns revealed reflect
behavioral differences and similarities between different groups. In
spite of a long tradition and a lot of research in cultural anthropology, sociology, and other fields that address cultural differences,
very little work has been carried out which takes into account large
data sets that specifically examine differences across countries1 .
In this paper we present a summary of some of our findings when
analyzing a large data set from Twitter. We perform this analysis
in order to examine possible differences and similarities in several
aspects of the use of the service. In particular, we focus on examining a year’s worth of Twitter data for a large number of “active” users in the ten countries which tweet the most. We report
on differences in terms of level of activity (number of tweets per
user), languages used per country, the happiness levels of tweets,
the content of tweets in terms of re-tweets, mentions, URLs, and
the use of hashtags. Additionally, we report differences and similarities in terms of the network structure. Our main contribution
is to provide a series of insights on how tweeting behavior varies
across countries, and on possible explanations for such differences.
To the best of our knowledge, this is the largest study done to date
Categories and Subject Descriptors
H.3.1 [Information Storage and Retrieval]: Content Analysis
and Indexing
General Terms
Measurement
Keywords
Social Media Analytics, Social Networks, Twitter
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CIKM’11, October 24–28, 2011, Glasgow, Scotland, UK.
Copyright 2011 ACM 978-1-4503-0717-8/11/10 ...$10.00.
INTRODUCTION
1
It is out of the scope of this paper to provide an in-depth review,
but here we refer specifically to analyzing differences in social media across different countries, as there has been of course a lot of
work on social network analysis of large data sets.
on microblogging data, and the first one that specifically examines
differences across different countries.
The rest of the paper is organized as follows. In Section 2 we
give a brief overview of related work and in Section 3 we describe
our data set. In Section 4 we describe the distribution of languages
used in each of the ten most active countries in our data set, and
the main findings of our analysis on the level of happiness in each
country. Section 5 focuses on the content of the tweets and network
structure, and we conclude by summarizing our main findings in
sections 6 and 7.
2.
RELATED WORK
The structure of social networks has been studied extensively because structure is strongly related to the detection of communities
and to how information propagates. Mislove et al. [10], for example, studied basic characteristics of the structure of Flickr, Orkut,
LiveJournal, and YouTube, and found Power-law, small-world, and
scale free properties. The authors argue that the findings are useful
in informing the design of social network-based systems. Kwak et
al. [9], examined the Twitter network aiming to determine it’s basic characteristics. One of their main findings is that Twitter does
not properly exhibit a “traditional" social network structure since
it lacks reciprocity (only 22% of all connections on Twitter were
found to be reciprocal), so it behaves more like news media, facilitating quick propagation of news. Java et al. [8], on the other
hand, studied the topological and geographical properties of Twitter’s social network and observed that there is high reciprocity and
the tendency for users to participate in communities of common
interest, and to share personal information. Onnela et al. [11],
present a study on a large-scale network of mobile calls and text
messages. They found no relationship between topological centrality and physical centrality of nodes in the network, and examined
differences among big and small communities.
One of the key questions relating to communities and network
structure is influence. De Choudhury et al. [4] examine Twitter data
and study how different sampling methods can influence the level
of diffusion of information. They found that sampling techniques
incorporating context (activity or location) and topology have better diffusion than if only context or topology are considered. They
also observed the presence of homophily, showing that users get
together with “similar" users, but that the diffusion of tweets also
depends on topics. Cha et al. [3] studied the in-degree and outdegree of the Twitter network and observed that influence is in fact
not related to the number of followers, but that having active followers who retweet or mention the user is more important.
3.
DATASET DESCRIPTION
Twitter is a platform which allows users to choose between keeping their profiles and activity (tweets) public or private. Users with
private profiles make their information available only to a selected
group of friends. For obvious reasons, we limit our research only
to information provided by users with public profiles2 .
The focus of our research is mostly on characterizing large online social networks, based on user geographical location, for which
Twitter provides limited information. Therefore, we perform an initial filter of users based on activity and profile information. First,
we choose users which we determine to be active. For this we examine a 10-day continuous time window of user activity, selecting
day-1 randomly from the year 2010. Then, we consider only as
2
All processing was anonymous and aggregated. No personal user
information was used
active, users which generated tweets during this time frame. Secondly, we filtered the resulting users to keep only active users which
had also entered a valid location into their profiles during this same
time period. We considered as a valid location any text which could
be parsed correctly into latitude and longitude (using the Yahoo!
public PlaceMaker API3 ). It should be noted, that we performed basically a static analysis, so we did not consider user mobility during
this period. We did not process location information which was automatically generated for the user with a GPS device on their client
application, based on the fact that location changes continuously
with the user. Since in this work we are interested more in characterizing geographical communities of users, we decided to use the
location which reflects more accurately the user’s home country.
Using this criteria, we obtained a set of 6, 263, 457 active users
with valid location information, which were divided into 246 different countries. For the rest of our analysis, we selected the Top-10
countries with more activity and gathered all of the tweets generated by these users for the entire duration of 2010. In total our
working dataset consisted of 4, 736, 629 users (76% of the initial
10-day user sample), and 5, 270, 609, 213 tweets. Figure 1 shows
the distribution of the users in our dataset into the top-10 countries,
and the activity that they generated for 2010. Note that the amount
of activity registered for each country is not necessarily proportional to the number of users. This is explicitly shown in Figure 2,
which displays the tweet/user ratio for each country. This ratio is
independent of the number of users in each local network.
4.
LANGUAGES AND SENTIMENT
Languages. To analyze the language in which tweets are written, we classify each tweet using proprietary software. As a result,
99.05% of the tweets were classified into 69 languages. The 10
most popular languages are shown in Figure 3. English is the most
popular language, and it corresponds to nearly 53% of the tweets.
Additionally, Figure 4 shows the three most common languages for
each of the top-10 countries, as well as the percentage of tweets
which correspond to these languages. It is worth noting that English is one of the three most frequently used languages for these
countries, and for the Netherlands, Indonesia, and Mexico more
than 10% of tweets are in English, while for Brazil it is 9%. Additionally, a special consideration should be taken for the languages
of Italian and Catalan, which appear in Figure 3 and Figure 4. This
is strange finding given the fact that Italy is not considered in the
top-10 countries of our study and the number of people who speak
Catalan world-wide is very small. By sampling the tweets for Catalan and Italian we find that many of them correspond to false positives given by our classifier, since they actually correspond to Portuguese and Spanish. The high resemblance of these languages,
in addition to the common use of slang, along with misspellings,
makes automatic language identification particularly challenging.
Sentiment Analysis. We also analyzed the sentiment component
of tweets, for this we use the measure of happiness as coined by
Dodds et al. [5], which is also more commonly referred to as valence. This value represents the psychological reaction which humans have to a specific word, according to a scale which ranges
from “happy” to “unhappy”. In particular, we analyze the happiness levels for each of the top-10 countries, considering only tweets
classified as English and Spanish. To achieve this, we used the 1999
Affective Norms for English Words (ANEW) list by Bradley and
Lang [1] for English tweets, and for Spanish, we used its adaptation by Redondo et al. [12]. The ANEW list contains 1,034 words
3
http://developer.yahoo.com/geo/placemaker/
Figure 3: Most commonly used languages in each top-10 country.
Figure 1: Distribution of users (%) in the dataset for each Top-10
country and their activity (%).
Figure 4: Three most popular languages for tweets in each top-10
country.
Figure 2: tweet/user ratio for each Top-10 country.
and each word has a score in a 1 to 9 range, which indicates its
level of happiness. We computed the “weighted average happiness
level", based on the algorithms of Dodds et al. [5], as follows:
PNl
Nl
X
i=1 wi fi,Cl
wi pi,Cl
happiness(Cl ) = P
=
(1)
Nl
i=1 fi,Cl
i=1
where happiness(Cl ) represents the weighted average happiness level for a country C, based on all of its tweets in language
l (English or Spanish), during 2010. Therefore Cl represents all
of the tweets registered for the country C which are expressed in
the language l. Additionally, Nl represents the number of words
in the ANEW list for the language l, while wi is the score for the
i-th word in the ANEW list for l, and fi,Cl corresponds to the frequency of this word in the collection Cl . Finally, we denote pi,Cl
as the normalized frequency of each sentiment scored word in Cl .
The results of this sentiment analysis for (a) English and (b) Spanish, are shown in Figure 5. These results agree with those reported
by Dodds et al. [5]: the values are between 5 and 7 for both languages and there is also a general increase in happiness towards the
end of the year. It is interesting to note that Brazil has the highest values almost every month, even though we are not particularly considering Portuguese. Nevertheless, after August happiness
levels in Brazil decreases until November. Also, in December all
countries show an increase in their happiness level.
Some differences can be appreciated in the results for Spanish
tweets, Figure 5.b. The number of tweets in Spanish is disproportional as 7 countries account for less than 1% of the tweets, while
Mexico, USA and Brazil together account for almost 98% of the total. Nevertheless, USA and Mexico have happiness patterns that are
similar to most countries. Only Brazil and Indonesia results which
differ from the rest: there is a strong increase in happiness from
June to July for Brazil and Indonesia. Interesting drops in levels
happen in Indonesia during the months of May and August. Brazil
has clearly the highest values for all months, but it also presents
higher ups and downs.
5.
CONTENT AND NETWORK STRUCTURE
Tweet Contents. In this part of our study, we analyze briefly certain tweet features for each top-10 country. These features have
also been used in prior work, such as [2]:
• #: indicates if a tweet contains a “#” symbol, which denotes a
tweet with a particular topic.
• RT: indicates if a tweet has a “RT”, which indicates a re-tweet
or re-post of a message of another user.
• @: indicates whether a tweet contains an “@” symbol, used
preceding a user name and which indicates a user mention.
• URL: Denotes whether a tweet contains a URL or not.
We computed the average per user for each country as follows:
PN T (symbol)ui
AVG(symbol ) =
i=1
TUi
PN
i=1
Ui
(2)
Where AVG(symbol ) is the average number of tweets per user
of a particular country containing a feature denoted by symbol (e.g.
#, RT, URL, @). Also, N is the total number of users for a particular country and T (symbol )Ui is the total number of tweets containing that feature for user Ui
Country
US
Brazil
UK
Japan
Canada
Indonesia
Mexico
Netherlands
South Korea
Australia
Users
1,616,702
688,427
286,520
133,536
132,240
130,943
112,793
86,863
80,381
67,416
Cov.(%)
12.47
5.31
2.21
1.03
1.02
1.01
0.87
0.67
0.62
0.52
Links
11,310,538
4,248,259
1,370,699
408,486
553,726
199,704
399,409
354,021
499,261
299,556
Cov.(%)
12.46
4.68
1.51
0.45
0.61
0.22
0.44
0.39
0.55
0.33
Recip.(%)
18.91
13.49
17.22
32.01
26.11
26.97
17.27
22.11
28.14
23.51
Table 2: General summary of network statistics per country.
Country
US
Brazil
Indonesia
United Kingdom
Japan
Mexico
Canada
South Korea
Netherlands
Australia
(a)
Avg. δ
8.95
7.55
2.12
6.05
4.36
4.44
5.73
8.61
5.39
5.83
Density
0.56 E-04
1.09 E-04
1.62 E-04
2.11 E-04
3.26 E-04
3.91 E-04
4.33 E-04
10.67 E-04
6.16 E-04
8.52 E-04
Avg. Clus. Coef.
0.0645
0.0711
0.0618
0.0933
0.0603
0.0826
0.1001
0.0879
0.1017
0.0959
Strongly CC
9,667
4,813
7,942
14,818
6,052
6,885
6,630
3,864
4,626
3,423
Table 3: Summary of network density statistics per country.
(b)
Figure 5: Average happiness level (a) English and (b) Spanish.
Country
Indonesia
Japan
Brazil
Netherlands
UK
US
Australia
Mexico
S. Korea
Canada
T weets
U sers
1813.53
1617.35
1370.27
1026.44
930.58
900.79
897.41
865.7
853.92
806
(U RL)%
14.95
16.30
19.23
24.40
27.11
32.64
31.37
17.49
19.67
31.09
(#)%
7.63
6.81
13.41
18.24
13.03
14.32
14.89
12.38
5.83
14.68
(@)%
58.24
39.14
45.57
42.33
45.61
40.03
43.27
49.79
58.02
42.50
(RT )%
9.71
5.65
12.80
9.12
11.65
11.78
11.73
12.61
9.02
12.50
Table 1: Average usage of features per user for each country
Table 1 shows the average per country as well as the ratio tweets
.
user
.
Results
show
Countries are ordered according to the ratio TUweets
ser
that Indonesia ranks first in tweets per user, followed by Japan and
Brazil. It is interesting also to see that Indonesia and South Korea
have the highest percentage of mentions in contrast to Japan that
has the lowest, and it seems also to be the country with the fewest
re-tweets in our data set. This indicates a higher use of Twitter
for conversation than in other countries. The Netherlands is the
country with most hashtags per user, while the US seems to be
the country with most mentions of URLs per user. At first glance,
this could indicate that the US uses Twitter more for formal news
dissemination, citing constantly external sources.
Network. Twitter also provides a social network structure for its
users. This is, users connect to each other through directed links,
therefore relationships are not necessarily reciprocal, as in Facebook4 . Users can choose to follow other users, by subscribing to
4
http://www.facebook.com
their updates. These connections between users can be viewed as a
large directed graph.
In this section we focus on the analysis of the Twitter social network graph for each top-10 country and its active users (as defined
in Section 3). In order to obtain this graph, we extracted user relationships using the public Twitter API (4J), collecting the list of
followers/followees for each user. In this particular graph, connections between users are highly dynamic, so we worked with a
snapshot of the graph, which was crawled between November 25
to December 2, 2010. This crawl resulted in 12, 964, 735 users and
90, 774, 786 edges. We cleaned this dataset to keep only edges and
users which corresponded to our active user set. Prior work [10]
has shown that analysis of partial crawls of social networks can
underestimate certain measures, such as degree distribution, but
continue to preserve accuracy for other metrics, such as density,
reciprocity and connectivity. Therefore, by preserving the active
component of the graph we are analyzing the most relevant part of
the social structure.
Table 2 shows a summary of each countries’ statistics. For each
local network analysis, we consider only connections between users
in the same country. The second and third columns in Table 2
show the node and edge coverage of each country in relation to
the complete graph. We also show the percent of reciprocity, which
is the fraction of ties between users which are symmetric. Overall,
the top-10 most active countries cover 25.73% of the total of active users in the social graph. Additionally, these countries cover
21.64% of the total number of edges in the global network. Table 2 shows that for some countries reciprocity is very significant
in particular for Japan, South Korea, Indonesia and Canada. The
symmetric nature of social ties affects network structure, increasing connectivity and reducing the diameter, as we show in the remaining of this work.
Table 3 shows a summary of graph density statistics, such as average degree (δ), density and average clustering coefficient. The US
and South Korea are the countries with the highest averaged degree
per node, meaning that users tend to concentrate more followers
and followees than in other countries. Indonesia, on the other hand,
presents a very low degree (only 2.12 edges per node on average)
Country
US
Brazil
Indonesia
United Kingdom
Japan
Mexico
Canada
South Korea
Netherlands
Australia
Modularity
0.418
0.462
0.537
0.397
0.458
0.358
0.568
0.312
0.412
0.452
Number of communities
2,954
2,896
3,358
2,486
1,998
1,406
1,269
756
936
634
Table 4: Summary of graph modularity statistics per country
Country
US
Brazil
Indonesia
United Kingdom
Japan
Mexico
Canada
South Korea
Netherlands
Australia
Diameter
18
16
35
15
18
16
16
11
17
14
Avg. Path length
6.49
6.37
9.69
5.19
5.26
5.27
4.71
4.02
4.81
4.52
Shortest paths
5,746,903,535
2,147,483,647
33,940,227
402,698,573
86,348,633
50,512,898
77,645,673
33,517,802
33,628,133
22,271,542
Table 5: Summary of graph distance measures per country
in spite of being a very active community. The second column in
Table 3 shows each local network’s density values. Density is comm
puted as n(n−1)
, where n is the number of nodes and m is the
number of edges. The density is 0 for a graph without edges and
1 for a fully connected graph. In our study, South Korea displays
the highest density of all countries. Additionally, density increases
as the network becomes smaller, i.e. the US has the lowest density,
and the highest values correspond to South Korea, Netherlands and
Australia. Therefore, smaller communities are in general more well
connected, within their own country.
The third column in Table 3 shows the average clustering coefficient. We can observe that communities with high clustering coefficient and less reciprocity may indicate more hierarchical-type
relationships between users (i.e., two users who share a reciprocal
tie follow a same third user who does not reciprocate). The fourth
column of Table 3 shows the number of strongly connected components that exist in each country.
Table 4 shows the the modularity of each social network graph,
we use this coefficient as defined by Girvan and Newman [6], which
evaluates how well a graph can be partitioned. A value of 0.4 or
greater is generally considered meaningful. In our analysis we
can appreciate that Indonesia and Canada display high modularity, which indicates that the communities found in these countries
are more compact and closed than in other countries. On the other
hand, Mexico, South Korea and United Kingdom indicate less separation between their communities. In addition, in Table 5 we summarize some general network distance measures per country. Table 5 shows that Indonesia presents the highest diameter, indicating that this network is very partitioned, which agrees with its high
modularity coefficient. Several countries register diameter values
in the range of 16-18. The lowest diameter is found South Korea.
We can also see that average path lengths are proportional to diameter values. In general, the number of shortest paths is proportional
to the number of edges in the graph. Notice that, for example,
the three graphs with the highest edge coverage values (US, Brazil
and United Kingdom) are also the three countries with the highest
shortest path values (see Table 2).
We analyze the existence of a direct relationship between average path length and diameter with reciprocity. Intuitively, we would
expect that shorter paths and diameters would result from networks
with high reciprocity. Nevertheless, experimentally, we do not observe any apparent relationship. On the contrary, several countries
show significant reciprocity and at the same time large diameters.
The most noticeable case is Indonesia, which shows the largest diameter and also high reciprocity. This suggests that graph structure
strongly influences the relationship between reciprocity and diameter. Given our previous observation, which was that Indonesia had
high modularity (see Table 4), this supports the idea that this country has very compact and isolated communities of users. On the
other hand, Canada also shows a very significant modularity value
but its diameter and average path length values are very similar to
countries that do not show a community structure. The main difference between Indonesia and Canada from our observation is that
the first has a much lower clustering coefficient and density than
the second. This might indicate that Indonesia has more users than
Canada which do not participate in large communities.
We also examine the graph structure of each network by considering node degree distribution. Degree distributions of many social
networks have been shown power laws behaviors. This kind of
networks are networks where the probability that a node has degree k is proportional to kγ , where γ is known as the power law
coefficient. Table 6 shows a summary of the graph degrees and
assortativity coefficient.
Country
US
Brazil
Indonesia
United Kingdom
Japan
Mexico
Canada
South Korea
Netherlands
Australia
In-degree
Power Law
9.51
7.56
6.21
7.31
7.48
5.91
8.31
6.36
6.67
7.72
Out-degree
Power Law
13.62
12.42
9.48
10.89
9.68
9.26
9.30
8.21
8.64
8.12
Assortativity
-0.19
-0.17
-0.06
-0.18
-0.07
-0.21
-0.11
-0.27
-0.17
-0.11
Table 6: Summary of graph degrees and assortativity statistics per
country
Finally, we analyze the number of in-links and out-links from
one community to another. These results are shown in Figure 6.
In this illustration it is interesting to observe that all countries direct the majority of their external out-links to the US. Nevertheless,
several countries concentrate their most significant amount of links
towards themselves, with the exceptions of Canada, Australia and
UK, which connect to the US almost as much as to themselves.
6.
SUMMARY AND DISCUSSION
Network reciprocity tells us about the degree of cohesion, trust
and social capital in sociology [7]. In this context, the equilibrium tendency in some human societies is to have reciprocal connections. Nevertheless, Twitter networks seem to work towards an
equilibrium which is not reciprocal and more hierarchical. Therefore, it tends more to follow a model in which we have authorities
which receive many ties but do not reciprocate.
On the other hand, we observe that countries which have high
reciprocity tend to have a higher tweets/user ratio. Additionally,
smaller networks also have a tendency to have higher reciprocity.
Indicating more local communities. Additionally, we see that communities which tend to be less hierarchical and more reciprocal,
In future work we expect to explore more formally correlations
between network structure measurements, hierarchy and happiness
levels. Also we would like to compare our findings with similar
research in other social networks, such as sociological studies.
Acknowledgments. This research was partially supported by European Community’s Seventh Framework Programme FP7/20072013 under the ARCOMEM project and by the Spanish Centre for
the Development of Industrial Technology under the CENIT program, project CEN-20101037 (www.cenitsocialmedia.es), “Social Media”. Additionally, this work has also been partially supported by FONDEF-CONICYT project D09I1185.
8.
Figure 6: Ties between countries, sizes are proportional the size of
the community. Weights represent fraction of ties estimated over
the total number of ties of a given country.
also displays happier language in their content updates. In this
sense countries with high conversation levels (@), shown in Table 1, display higher levels of happiness too. This is reasonable, if
we think that higher conversational levels can imply that users privilege more informal communication, as opposed to formal news
dissemination. Following this reasoning, we can hypothesize that
these users, use Twitter more as a conversation channel than formal
information source. Therefore, their interaction is highly conversational with friends, as opposed to being more formal. which is the
case of the US. High reciprocity does not imply that these countries
are more well connected overall, in cases such as Indonesia, reciprocity is contained within very compact and closed groups. This
increases the overall diameter of the network, as seen in Table 4.
By analyzing Table 3, we can observe that smaller communities, tend to present more reciprocal ties between users in general,
along with high density and average clustering coefficient (not always, but this is the tendency). Pointing towards more reciprocal
relationships. Additionally, high reciprocity creates high activity in
communities such as Indonesia and Japan. On the other hand, we
can see in Table 3 that countries with high density and high clustering coefficient, such as South Korea, Netherlands and Australia,
contain users which participate in small and compact communities.
Other countries, such as the US, have low clustering coefficient
and density, which indicates that many users do not participate in a
small compact community.
7.
CONCLUSIONS
We have presented a broad study of the Twitter on-line social
network. We have segmented our analysis into the top-10 countries
with most activity and collected data from a representative sample
of users for one year. We analyze several aspects, such as language,
sentiment, content and network properties.
In particular some countries standout, based on their peculiar
characteristics. Some smaller networks display high reciprocity
and more conversation. This indicates a high conversational use
within compact communities, as opposed to broad dissemination of
news information. In this sense, there also seems to be a correlation
between reciprocity and levels of happiness detected in language,
examples are Indonesia and Brazil. On the other hand, users in the
US give Twitter a more informative purpose, which is reflected in
more globalized communities, which are more hierarchical.
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